pacman::p_load(tmap, sf, DT, stplanr,
performance,
ggpubr, tidyverse)Hands-on Exercise 3
1.1 Overview
In this hands-on exercise, you will learn how to build an OD matrix by using Passenger Volume by Origin Destination Bus Stops data set downloaded from LTA DataMall. By the end of this hands-on exercise, you will be able:
to import and extract OD data for a selected time interval,
to import and save geospatial data (i.e. bus stops and mpsz) into sf tibble data frame objects,
to populate planning subzone code into bus stops sf tibble data frame,
to construct desire lines geospatial data from the OD data, and
to visualise passenger volume by origin and destination bus stops by using the desire lines data.
1.2 Getting Started
1.3 Preparing the Flow Data
1.3.1 Importing the OD data
odbus <- read_csv("data/aspatial/origin_destination_bus_202310.csv")glimpse(odbus)Rows: 5,694,297
Columns: 7
$ YEAR_MONTH <chr> "2023-10", "2023-10", "2023-10", "2023-10", "2023-…
$ DAY_TYPE <chr> "WEEKENDS/HOLIDAY", "WEEKDAY", "WEEKENDS/HOLIDAY",…
$ TIME_PER_HOUR <dbl> 16, 16, 14, 14, 17, 17, 17, 7, 14, 14, 10, 20, 20,…
$ PT_TYPE <chr> "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "…
$ ORIGIN_PT_CODE <chr> "04168", "04168", "80119", "80119", "44069", "2028…
$ DESTINATION_PT_CODE <chr> "10051", "10051", "90079", "90079", "17229", "2014…
$ TOTAL_TRIPS <dbl> 3, 5, 3, 5, 4, 1, 24, 2, 1, 7, 3, 2, 5, 1, 1, 1, 1…odbus$ORIGIN_PT_CODE <- as.factor(odbus$ORIGIN_PT_CODE)
odbus$DESTINATION_PT_CODE <- as.factor(odbus$DESTINATION_PT_CODE) 1.3.2 Extracting the study data
odbus6_9 <- odbus %>%
filter(DAY_TYPE == "WEEKDAY") %>%
filter(TIME_PER_HOUR >= 6 &
TIME_PER_HOUR <= 9) %>%
group_by(ORIGIN_PT_CODE,
DESTINATION_PT_CODE) %>%
summarise(TRIPS = sum(TOTAL_TRIPS))datatable(odbus6_9)write_rds(odbus6_9, "data/rds/odbus6_9.rds")odbus6_9 <- read_rds("data/rds/odbus6_9.rds")1.4 Working with Geospatial Data
1.4.1 Importing geospatial data
busstop <- st_read(dsn = "data/geospatial",
layer = "BusStop") %>%
st_transform(crs = 3414)Reading layer `BusStop' from data source
`/Users/pirapatchaiya/Library/Mobile Documents/com~apple~CloudDocs/SMU MITB NOV TERM/Applied Geospatial Analytics/ISSS624/ISSS624/Hands-on_Ex/Hands-on_Ex3/data/geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 5161 features and 3 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 3970.122 ymin: 26482.1 xmax: 48284.56 ymax: 52983.82
Projected CRS: SVY21mpsz <- st_read(dsn = "data/geospatial",
layer = "MPSZ-2019") %>%
st_transform(crs = 3414)Reading layer `MPSZ-2019' from data source
`/Users/pirapatchaiya/Library/Mobile Documents/com~apple~CloudDocs/SMU MITB NOV TERM/Applied Geospatial Analytics/ISSS624/ISSS624/Hands-on_Ex/Hands-on_Ex3/data/geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS: WGS 84mpszSimple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21 / Singapore TM
First 10 features:
SUBZONE_N SUBZONE_C PLN_AREA_N PLN_AREA_C REGION_N
1 MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
2 INSTITUTION HILL RVSZ05 RIVER VALLEY RV CENTRAL REGION
3 ROBERTSON QUAY SRSZ01 SINGAPORE RIVER SR CENTRAL REGION
4 JURONG ISLAND AND BUKOM WISZ01 WESTERN ISLANDS WI WEST REGION
5 FORT CANNING MUSZ02 MUSEUM MU CENTRAL REGION
6 MARINA EAST (MP) MPSZ05 MARINE PARADE MP CENTRAL REGION
7 SUDONG WISZ03 WESTERN ISLANDS WI WEST REGION
8 SEMAKAU WISZ02 WESTERN ISLANDS WI WEST REGION
9 SOUTHERN GROUP SISZ02 SOUTHERN ISLANDS SI CENTRAL REGION
10 SENTOSA SISZ01 SOUTHERN ISLANDS SI CENTRAL REGION
REGION_C geometry
1 CR MULTIPOLYGON (((33222.98 29...
2 CR MULTIPOLYGON (((28481.45 30...
3 CR MULTIPOLYGON (((28087.34 30...
4 WR MULTIPOLYGON (((14557.7 304...
5 CR MULTIPOLYGON (((29542.53 31...
6 CR MULTIPOLYGON (((35279.55 30...
7 WR MULTIPOLYGON (((15772.59 21...
8 WR MULTIPOLYGON (((19843.41 21...
9 CR MULTIPOLYGON (((30870.53 22...
10 CR MULTIPOLYGON (((26879.04 26...1.5 Geospatial data wrangling
1.5.1 Combining Busstop and mpsz
busstop_mpsz <- st_intersection(busstop, mpsz) %>%
select(BUS_STOP_N, SUBZONE_C) %>%
st_drop_geometry()datatable(busstop_mpsz)write_rds(busstop_mpsz, "data/rds/busstop_mpsz.rds") od_data <- left_join(odbus6_9 , busstop_mpsz,
by = c("ORIGIN_PT_CODE" = "BUS_STOP_N")) %>%
rename(ORIGIN_BS = ORIGIN_PT_CODE,
ORIGIN_SZ = SUBZONE_C,
DESTIN_BS = DESTINATION_PT_CODE)duplicate <- od_data %>%
group_by_all() %>%
filter(n()>1) %>%
ungroup()od_data <- unique(od_data)od_data <- left_join(od_data , busstop_mpsz,
by = c("DESTIN_BS" = "BUS_STOP_N")) duplicate <- od_data %>%
group_by_all() %>%
filter(n()>1) %>%
ungroup()od_data <- unique(od_data)od_data <- od_data %>%
rename(DESTIN_SZ = SUBZONE_C) %>%
drop_na() %>%
group_by(ORIGIN_SZ, DESTIN_SZ) %>%
summarise(MORNING_PEAK = sum(TRIPS))write_rds(od_data, "data/rds/od_data.rds")od_data <- read_rds("data/rds/od_data.rds")1.6 Visualising Spatial Interaction
In this section, we will learn how to prepare a desire line by using stplanr package.
1.6.1 Removing intra-zonal flows
od_data1 <- od_data[od_data$ORIGIN_SZ!=od_data$DESTIN_SZ,]1.6.2 Creating desire lines
flowLine <- od2line(flow = od_data1,
zones = mpsz,
zone_code = "SUBZONE_C")1.6.3 Visualising the desire lines
tm_shape(mpsz) +
tm_polygons() +
flowLine %>%
tm_shape() +
tm_lines(lwd = "MORNING_PEAK",
style = "quantile",
scale = c(0.1, 1, 3, 5, 7, 10),
n = 6,
alpha = 0.3)
tm_shape(mpsz) +
tm_polygons() +
flowLine %>%
filter(MORNING_PEAK >= 5000) %>%
tm_shape() +
tm_lines(lwd = "MORNING_PEAK",
style = "quantile",
scale = c(0.1, 1, 3, 5, 7, 10),
n = 6,
alpha = 0.3)